AI and Synthetic Biology: The New Frontier of Promise and Power
By Martyna Chmura | 12 November 2025
Summary
Artificial intelligence is reshaping synthetic biology by accelerating the pace, scale, and ambition of biological innovation. Generative algorithms now design proteins and DNA, opening vast new opportunities in health, climate and manufacturing, while exposing societies to new security and ethical risks.
AI-enabled biology lowers costs and accelerates discovery, promising major economic and environmental benefits. Yet the same acceleration widens governance gaps, increases dual-use risks, and concentrates power in a few states and corporations.
Rapid diffusion of AI-biodesign tools is highly likely to drive widespread innovation and moderate regulatory frictions; scaling of automated biofoundries and infrastructure competition is likely to reshape industrial supply chains; and geopolitical consolidation of “bio-AI powers” is possible to redefine global governance structures.
Background
Synthetic biology applies engineering principles to life: Designing DNA sequences, constructing new biological systems, and reprogramming organisms for specific functions. When paired with AI through deep learning, generative models and automation, it is evident that the field is transitioning from manual trial-and-error to rapid, algorithmic design and testing.
DeepMind’s AlphaFold2 demonstrated this transformation by predicting near-atomic protein structures for nearly all known proteins, a contribution recognised in the 2024 Nobel Prize in Chemistry. Subsequent models like RoseTTAFold and EvoDiff now design new proteins altogether, while AI-assisted “biofoundries” automate synthesis and testing, shortening discovery cycles drastically.
While this capability opens enormous promises, such as protein therapeutics, enzyme-based carbon capture, synthetic materials, governance and infrastructure often lag behind. Reports highlight that AI-enabled synthetic biology can bring dual-use risks and exceed traditional biosafety frameworks.
Implications
The Nobel recognition symbolises not just scientific achievement but a strategic inflexion point. The capacity to design biology at digital speed carries profound economic, environmental, and security implications.
Economically, AI-enabled biotech could reshape sectors from pharmaceuticals to climate tech to sustainable agriculture. Automated design-build-test cycles reduce R&D costs and compress innovation timelines, creating new markets and jobs while attracting record investment. The global AI-in-synthetic-biology market was valued at around USD 94.7m in 2024 and is projected to reach USD 438.4m by 2034 (CAGR ~16.6 %). In Europe, the biotechnology startup Isomorphic Labs raised EUR 523m in 2025 alone, highlighting the region’s growing interest in AI-driven drug discovery and precision medicine. Yet the same acceleration deepens dependency on semiconductor supply, cloud compute and proprietary datasets, making strategic assets concentrated among a few global players.
Environmentally, AI-generated enzymes and engineered microbes could accelerate carbon capture and waste recycling, but premature environmental release risks gene transfer and unintended ecological impacts if regulation lags. Strategically, the dual-use nature of AI-biology heightens biosecurity concerns. Generative algorithms that can design therapeutic proteins can also be misused to design toxins or novel pathogens. Reports warn that AI-designed sequences could evade existing DNA-synthesis screening and detection mechanisms. Socially, the democratisation of AI-bio tools may widen disparities between states and firms. Wealthier nations equipped with computing and synthesis capacity will likely dominate bio-industrial ecosystems, while others face reliance on foreign data or cloud infrastructure.
Governance frameworks are also not adequately prepared. The Biological Weapons Convention (BWC) lacks inspection and digital monitoring mechanisms and remains reliant on voluntary declarations, while the World Health Organisation’s (WHO) International Health Regulations focus on infectious-disease outbreaks, not algorithmic generation of biological code. The EU AI Act also creates obligations for transparency and risk classification but does not address AI-enabled synthetic biology or cross-border code flow.
Sangharsh Lohakare/Unsplash
Forecast
Short-term (Now - 3 months)
It is almost certain that AI-biodesign tools will proliferate rapidly across research and industry, outpacing ethical review and biosafety oversight.
Highly likely that major cloud providers will expand integrated AI-biofoundry services, concentrating innovation infrastructure under a few technological actors.
Medium-term (3-12 months)
Uptake in large-scale automation and synthesis pipelines are likely to strain global supply chains and intensify competition for compute and reagents.
There is a realistic possibility that a data or sequence security incident involving AI-generated code will prompt more restrictive public and dataset-sharing standards.
Long-term (>1 year)
It is likely that a small group of “bio-AI powers” combining compute, synthesis and governance capacity will dominate global biotechnology and standard-setting.
It is unlikely that security and attribution challenges from AI-generated biological components will drive negotiations on international oversight mechanisms under the BWC or WHO.
